python算法学习之计数排序实例
代码如下:
# -*- coding: utf-8 -*-
def _counting_sort(A, B, k):
"""计数排序,伪码如下:
COUNTING-SORT(A, B, k)
1 for i ← 0 to k // 初始化存储区的值
2 do C[i] ← 0
3 for j ← 1 to length[A] // 为各值计数
4 do C[A[j]] ← C[A[j]] + 1
5 ▷ C[i]包含等于i的元素个数
6 for i ← 1 to k // 求计数和,确定 7 do C[i] ← C[i] + C[i-1]
8 ▷ C[i]包含小于或等于i的元素个数
9 for j ← length[A] downto 1
10 do B[C[A[j]]] ← A[j] // 将A[j]值放到对应位置
11 C[A[j]] ← C[A[j]] - 1 // 避免元素相同时覆盖同一位置
T(n) = θ(n)
Args:
A (Sequence): 原数组
B (Sequence): 结果数组
k (int): 值上限,假定了所有元素介于[0,k]
"""
len_c = k + 1
C = [0] * len_c
for a in A:
C[a] = C[a] + 1
for i in range(1, len_c):
C[i] = C[i] + C[i-1]
for a in A[::-1]:
B[C[a]-1] = a
C[a] = C[a] - 1
def counting_sort(A):
"""假定A数组所有元素都介于[0,len(A)-1]"""
B = [0] * len(A)
_counting_sort(A, B, len(A) - 1)
return B
if __name__ == '__main__':
import random, timeit
items = range(10000)
random.shuffle(items)
def test_sorted():
print(items)
sorted_items = sorted(items)
print(sorted_items)
def test_counting_sort():
print(items)
sorted_items = counting_sort(items)
print(sorted_items)
test_methods = [test_sorted, test_counting_sort]
for test in test_methods:
name = test.__name__ # test.func_name
t = timeit.Timer(name + '()', 'from __main__ import ' + name)
print(name + ' takes time : %f' % t.timeit(1))

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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